Investigation of Appropriate Collector Selection for Hematite Removal from Pyrolusite and the Adsorption Mechanism on the Crystal Surface

Abstract

This study examined the appropriate hematite (Fe₂O₃) collector for the concentration of pyrolusite (MnO₂) in a reverse flotation. Actual ore flotation studies were performed to determine how sodium oleate, sodium dodecyl sulfonate, and oxidized paraffin soap affect hematite removal during reverse flotation of pyrolusite ore. In order to explore the flotation mechanism, simulation experiments were carried out. Firstly, the crystal models of pyrolusite and hematite were established. Then, in order to verify the reliability of the simulation results, the simulated XRD spectra of the crystal model were compared with the measured spectra. Finally, density functional theory and molecular dynamics modeling were used to study the interaction between collector molecules and mineral surfaces. The flotation test results show that oxidized paraffin soap is the best hematite collector and promotes its flotation, removing iron from pyrolusite. Molecular dynamics simulations and density functional theory show that the three collectors (oxidized paraffin soap, sodium oleate, and sodium dodecyl sulfonate) have a much stronger interaction with hematite than with pyrolusite. Therefore, it is possible to separate pyrolusite and hematite through flotation. The simulation results also show that oxidized paraffin soap has the highest adsorption strength and selectivity for hematite. This characteristic makes oxidized paraffin soap an excellent collector for effectively removing hematite from pyrolusite in the reverse flotation process.

1. Introduction

Manganese is a crucial primary metal resource that is extensively utilized in several industries such as metallurgy, construction materials, renewable energy, medicine, and other sectors [1]. The abundance of valuable minerals is primarily found in certain nations, namely South Africa, Australia, and Brazil. Conversely, countries like China and Ukraine possess manganese ore with lower grades (below 30%), necessitating beneficiation processes to make it suitable for use. The global issue lies in the beneficiation and enrichment of low-grade manganese ore due to its intricate composition and the significant occurrence of impurities, i.e., minerals like hematite [2,3,4,5].

For a long time, magnetic separation has been dominant in manganese ore beneficiation. Manganese minerals are weakly magnetic minerals, and the use of strong magnetic separation can achieve the separation and enrichment of manganese minerals. However, with the continuous increase in manganese ore development, the proportion of difficult-to-select manganese ore resources, such as fine particle embedding and the complex coexistence of multiple metals, is increasing. A single magnetic separation method makes it difficult to efficiently and reasonably utilize manganese ore resources.

The flotation method is a good choice for processing the difficult-to-select manganese ore resources with microparticle embedding or multi-metal coexistence mentioned above [6]. However, due to the poor floatability of manganese oxide ore, the development of positive flotation technology for manganese oxide ore is difficult. Compared to positive flotation, reverse flotation is a process where the collector acts on the surface of silicate and other gangue minerals [7,8,9]. The flotation effect depends on the difference in planktonic activity between manganese minerals and gangue minerals under the action of the reverse flotation collector, which can be technically achieved [10,11,12,13].

The purpose of this article is to explore the reverse flotation collector of pyrolusite under the condition of hematite as gangue and to study its reverse flotation mechanism. Firstly, the selection of exceptional collectors for investigating soft manganese ore is conducted by practical ore flotation trials. Using Materials Studio software (version 8.0), crystal structures of pyrolusite and hematite were constructed. Additionally, the molecular structures of sodium dodecyl sulfate, sodium oleate, and oxidized paraffin soap were examined. Subsequently, the density functional theory (DFT) is employed to compute the density of states and the energy of frontier orbitals in mineral crystals. Once the adsorption arrangement of the collector crystal surface was determined, molecular dynamics techniques were employed to compute the adsorption energy between the two surfaces.

2. Materials and Methods

2.1. Preparation of Mineral Samples

The unprocessed mineral originates from Yunnan Province, China, and it underwent magnetic separation using a laboratory magnetic separator (CRIMM DCJB70-200) to obtain a sample with a grade of 27.36%. Based on the chemical multi-element analysis (

Table 1. Results of multiple element analysis of magnetic concentrate.

Element	Mn	Fe	S	P	SiO2	Al2O3
Content/%	27.36	11.60	0.084	0.07	18.26	2.23

) and MLA detection (

Table 2. Quantitative detection results of raw ore minerals.

Mineral	Content/%	Mineral	Content/%
Pyrolusite	10.698	Rutile	0.108
Titanomagnetite	0.015	Zoisite	0.003
Hematite	6.215	Fluorite	0.002
Pyrite	0.004	Calcite	0.004
Kaolin	34.830	Dolomite	0.004
Quartz	45.398	Apatite	0.001
Feldspar	1.83	Sphene	0.025
Sericite	0.121	Zircon	0.003
Flogopite	0.221	Other	0.094
Tourmaline	0.424	Total	100

), the sample may be classified as a typical pyrolusite. The major form of manganese (Mn) in the sample is pyrolusite, whereas iron (Fe) is mostly present as hematite and limonite.

2.2. Flotation Tests Method

Firstly, 500 g of ore sample was accurately weighed and placed in a 1500 mL flotation cell (Flotation machine model XFD3). Water was added to the cell until the slurry concentration reached 33%, and the slurry was stirred at a speed of 1992 r/min for 2 min. Then, pH regulators (Na₂CO₃), inhibitors (NaSiO₃), and collectors (sodium oleate, sodium dodecyl sulfonate, or oxidized paraffin soap) were sequentially introduced at intervals of 3 min, 2 min, and 2 min, respectively. After that, foam was skimmed for 5 min under suitable pH conditions to remove foam products. Finally, the collector was reintroduced to facilitate flotation, and the substrate of the cell was the pyrolusite concentrate. Figure 1 illustrates the procedure of flotation experimentation.

2.3. Simulation Methods and Parameters

The calculations were performed using the CASTEP, Dmol³, and Forcite modules of the Materials Studio software (MS), based on the first principles of density functional theory and molecular dynamics. The BFGS optimization algorithm was employed to optimize the structure of the original unit cell models of pyrolusite and hematite. The PBE gradient correction function, based on the generalized gradient approximation (GGA), was utilized. The interaction between valence electrons and ions was elucidated utilizing a very soft pseudopotential. The convergence requirements were defined as follows: the maximum allowed atomic movement was set at 0.0001 nm, the maximum allowed interatomic force was set at 0.03 eV/nm, and the maximum allowed interatomic internal stress was set at 0.05 GPa. The total energy change of the system did not exceed 1.0 × 10⁻⁵ eV/atom. All calculations were performed in reciprocal space, considering the electron–spin interactions of each atom. The electronic configurations employed for calculation were Fe: 3d⁶4s², O: 2s²2p⁴, and Mn: 3d⁵4s².

The collector’s frontier molecular orbitals were computed using the DMol³ modules, following their optimization with CASTEP. The calculating parameters of the collector were in accordance with the optimization of the mineral’s bulk and surface.

Based on the frontier orbital theory, stronger interactions occur when the absolute values of the difference (|ΔE|) between the highest occupied molecular orbital (HOMO) energy of the reagent molecule (E_reagent) and the lowest unoccupied molecular orbital energy (LUMO) of minerals (E_mineral) are smaller. The LUMO and HOMO were determined through the utilization of the correlation function of GGA-PBE while maintaining a fixed self-consistent field convergence threshold of 1.0 × 10⁻⁵ eV/atom.

According to the literature, the most thermodynamically stable surfaces of pyrolusite and hematite are (110) and (104), respectively [14]. The structures of these two crystal planes were established using MS, with a layer thickness of 4 Å and a vacuum layer thickness of 50 Å. The topmost three atomic layers on the constructed surface were allowed to relax, whereas the underlying atomic layers were fixed.

Then, the model was first optimized through the geometry optimization task, during which the algorithm of smart, the forcefield of universal, and the charges using QEq were fixed, and the convergence tolerance, the force tolerance, and the displacement tolerance were set to 0.0001 kcal/mol, 0.005 kcal/mol/Å, and 5.0 × 10^–5 Å, respectively. The flotation reagent molecules were also geometry optimized in a 50 Å × 50 Å × 50 Å cubic cell by Forcite, during which the algorithm of smart, the universal forcefield, and the charges using QEq were fixed, and the convergence tolerance and the force tolerance were set to 0.001 kcal/mol and 0.5 kcal/mol/Å, respectively.

In order to obtain the optimal configuration, molecular dynamic simulations were conducted through the quench task, during which the forcefield of universal, the charges using QEq, and the NVE ensemble at a time step of 1.0 fs for a total simulation time of 150 at 298.0 k were fixed. The van der Waals interaction cutoff distance was 9.5 Å, and the Ewald method was adopted to calculate the long-range electrostatic interactions with a higher accuracy of 10⁻³ kcal/mol. After molecular dynamic simulations, the optimal configurations of flotation reagent molecules on mineral surfaces were obtained, and the interaction energies were calculated using the method shown in Equation (1).

ΔE = E_Total − E_Reagent − E_Mineral

(1)

where E_Total, E_Reagent, and E_Mineral are total energies of the optimal configurations, flotation reagent molecules, and mineral surfaces, respectively. Theoretically, the more negative the interaction energy, the more powerful the adsorption of the reagents to the mineral surfaces is.

3. Results and Discussion

3.1. Flotation Test Results

Flotation studies were conducted at a temperature of 25 °C and a slurry pH of 11 to examine the efficiency of sodium dodecyl sulfonate (SDS), sodium oleate, and oxidized paraffin soap in removing hematite and collecting it. The results are shown in Figure 2.

The results show that at the same dosage, the collector types have little effect on hematite grade, but different collectors have a significant effect on hematite recovery. The hematite recovery rate using oxidized paraffin soap as a collector is significantly higher than that of the other two collectors. It can also be seen from the experimental results that when the amount of collectors is in the range of 100 g/t to 300 g/t, the grade and recovery of hematite gradually increase with the increase in the amount of collector. When the collector dosage exceeded 300 g/t, although the hematite recovery rate still showed an upward trend, the grade began to decline. Therefore, the optimal dosage of oxidized paraffin soap for pyrolusite reverse flotation is 300 g/T.

3.2. Construction and Analysis of Pyrolusite and Hematite Crystals

To investigate the mechanism of interaction between oxidized paraffin soap and the two minerals, quantum mechanical and molecular dynamics simulations were conducted. Typical mineral crystal models were used for simulation calculations, as shown in Figure 3.

3.2.1. Structure Optimization of Unit Cell

Firstly, the PBE gradient correction function was used to perform convergence tests on the k-point and cutoff parameters of the crystal cells of pyrolusite and hematite, respectively. The results are shown in Figure 4 and Figure 5.

The results of pyrolusite unit cell testing indicate that with the increase in cutoff energy and k-point sampling density, the total energy of the system gradually decreases. A cutoff energy of 500 eV and a k-point sampling density of 2 × 2 × 3 were adopted in the subsequent calculation. These parameters can ensure calculation accuracy and save computational resources at the same time.

The results of hematite unit cell testing indicate that with the increase in cutoff energy and sampling density at k-point, the total energy of the system gradually decreases. A cutoff energy of 600 eV and a k-point sampling density of 6 × 6 × 6 were adopted in the subsequent calculation. These parameters can ensure calculation accuracy and save computational resources at the same time.

To verify the accuracy of the simulation calculation results, the XRD of the simulated unit cell was compared with the spectrum of the pure mineral (Figure 6). Simulated unit cell XRD was analyzed using the Reflex module, while the XRD spectra of actual minerals were obtained from the previous studies, respectively [15,16]. The findings show that the XRD spectra of the simulated crystal cells agree well with the mineral measurement results. This shows that the simulation method is accurate and reliable.

3.2.2. Crystal Properties of Pyrolusite and Hematite

The density of states of pyrolusite was calculated, and the results are presented in Figure 7. The valence band of pyrolusite is mainly composed of p and d orbitals, with O atom p orbitals and Mn atom d orbitals dominating, while Mn atom p orbitals contribute less. Near the Fermi level, the main contributions are the 2p orbitals of the O atom and the 3d orbitals of the Mn atom, with the 3d orbitals of the Mn atom contributing the most.

The density of states of hematite was calculated, and the results are presented in Figure 8. The lowest energy band of hematite crystal is mainly in the s state, with the contribution of the O atom’s 2s orbital being the largest, followed by the Fe atom’s 4s orbital. The valence band of −10~0 eV is mainly composed of p and d components, with O atom 2p orbitals and Fe atom 3d orbitals being the main components. Near the Fermi level, it is mainly composed of O atom 2p orbitals and Fe atom 3d orbitals, with Fe atom 3d orbitals contributing the most. From its density of states graph, it can be seen that the 3d orbitals of Fe atoms are located near the Fermi level and filled with more electrons. Therefore, for hematite crystals, the Fe atoms in their structure are more active and can act as active centers for reactions.

3.2.3. Distribution Analysis of Pyrolusite and Hematite

Atomic and overlap population calculations were performed on the pyrolusite unit cell, as shown in

Table 3. Mulliken population of pyrolusite.

Species	s	p	d	Total	Charge/e
Mn	0.18	0.22	4.82	5.22	0.98
Mn	1.72	0.81	0.00	1.79
O	0.96	2.56	0.00	3.52	−0.66
O	0.93	2.21	0.00	3.14

and

Table 4. Mulliken overlap population of pyrolusite.

Bond Species	Overlap Population	Bond Length/Å
Mn-O	0.29	1.98
Mn-O	0.19	2.07
Mn-Mn	−0.47	2.71
O-O	−0.07	2.72
O-O	−0.06	2.80
O-O	−0.05	2.89
Mn-Mn	−0.63	2.93

, respectively.

The charge population results of pyrolusite show that the peripheral electron configuration of Mn atoms before optimization is 4s²3d⁵, and the optimized electronic configuration is 3p^0.224s^0.183d^4.82. The charge of Mn atoms is +0.98 e, indicating that the Mn atom is an electron-deficient site. The electronic configuration of the O atom before optimizing is 2s²2p⁴. After optimization, the electronic configuration becomes 2s^0.932p^2.21. The charge of the O atom is −0.66 e, indicating that the O atom is the electron-rich site. The overlap population of Mn-O is relatively small, and there is a significant charge transfer between Mn and O, indicating that the covalency of Mn-O is weak and the ionization is strong.

Atomic and overlap population calculations were performed on the hematite unit cell, as shown in

Table 5. Mulliken charge population of hematite.

Species	s	p	d	Total	Charge/e
Fe	0.20	0.24	4.79	5.23	1.02
O	0.95	2.56	0.00	3.51	−0.68

and

Table 6. Mulliken overlap population of hematite.

Bond Species	Overlap Population	Bond Length/Å
Fe-O	0.29	1.95
Fe-O	0.22	2.05
Fe-Fe	−0.16	2.66
Fe-Fe	−0.68	2.89
O-O	−0.08	2.68
O-O	−0.03	2.77

, respectively.

The atomic population results of hematite show that the peripheral electron configuration of Fe atoms before optimization is 4s²3d⁶, and the optimized electronic configuration is 3p^0.244s^0.203d^4.79. The charge of Fe atoms is +1.02 e, indicating that the Fe atom is an electron-deficient site. The electronic configuration of the O atom before optimizing is 2s²2p⁴. After optimization, the electronic configuration becomes 2s^0.932p^2.21. The charge of the O atom is −0.66 e, indicating that the O atom is an electron-rich site. The overlap population of Fe-O is relatively small, and there is a significant charge transfer between Fe and O, indicating that the covalency of Fe-O is weak and the ionization is strong.

3.2.4. Frontier Molecular Orbital (FMO) Analysis

As per the FMO theory, the ease of interaction between the collector molecule and the mineral depends on the magnitude of the difference between the energy levels of the highest occupied molecular orbital (HOMO) of the collection molecule and the lowest unoccupied molecular orbital (LUMO) of the mineral. A lower absolute difference indicates a greater likelihood of interaction. The theory was utilized to optimize the structure of SDS, sodium oleate (NaOl), and oxidized paraffin soap (OPS) using the Dmol³ module. Figure 9 displays the molecular structure that has been optimized.

The FMOs of pyrolusite, hematite, and collector molecules were calculated, and the frontier orbital energy differences between reagents and minerals are shown in

Table 7. Frontier orbital energy differences between reagents and minerals.

Mineral/Collector	EHOMO/eV	ELUMO/eV	|ΔE1|/eV	|ΔE2|/eV	|ΔE3|/eV
Pyrolusite	−7.368	−5.546	1.126	1.142	0.175
Hematite	−5.002	−4.682	0.262	0.278	0.690
OPS	−4.420	−1.233
NaOl	−4.404	−1.204
SDS	−5.372	−1.979

Note (1): |ΔE₁| is the absolute difference between the HOMO of OPS and the LUMO of mineral. Note (2): |ΔE₂| is the absolute difference between the HOMO of NaOl and the LUMO of minerals. Note (3): |ΔE₃| is the absolute difference between the HOMO of SDS and the LUMO of minerals.

.

The findings show that the three collectors’ interaction abilities with pyrolusite rank from strong to weak, as follows: SDS > OPS > NaOl. The three collectors’ interaction abilities with hematite rank from strong to weak, as follows: OPS > NaOl > SDS. According to the results, the interaction strength between three collectors and pyrolusite is significantly weaker than that of hematite. When using OPS as a collector, it will primarily adsorb on the surface of hematite, providing a theoretical basis for the flotation separation of pyrolusite and hematite. Moreover, the interaction strength between OPS and hematite is more intense than the other two, which is consistent with the flotation test results.

3.3. Molecular Dynamics Simulation of Collector–Mineral Interaction

The energy difference between the frontier orbitals can be used to compare and analyze the possibility of adsorption between the collector and the mineral surface. Additionally, the adsorption energy can be used to figure out the adsorption strength of the two substances in a more intuitive manner. In order to achieve this objective, molecular dynamics simulations were performed using the Forcite module of the Materials Studio program, and the adsorption energy was computed. Section 2.3 presents the molecular dynamics simulation technique.

The optimized collector molecule was placed above the optimized mineral surface to construct the initial configuration of the interaction. For the initial configuration, the Quench function was used for dynamic simulation calculations, and the temperature was 298 K. The electrostatic interactions between atoms were calculated using the modified summation method, while the intermolecular forces were calculated using the atom-based direct truncation method, with a truncation length of 9.5 Å. When the energy gradient reached 10⁻³ kcal/mol, the optimization process reached convergence. After molecular dynamics simulation calculations, the optimal configurations of collector molecules on the mineral surface were obtained. The results of the molecular dynamics calculation are shown, respectively, in Figure 10, Figure 11 and Figure 12.

Figure 10 shows the simulation results of the interaction between SDS and minerals. The results indicate that oxygen atoms are the main sites of action for SDS anions, while metal atoms are the main sites of action on mineral surfaces. The distance between SDS and pyrolusite, as well as between SDS and hematite, is relatively far, indicating weak interaction between SDS and these two minerals.

Figure 11 displays the results of the NaOl–mineral molecular dynamics simulation. The distance of NaOl–hematite is significantly farther than that of NaOl–pyrolusite, indicating that the NaOl–hematite interaction is stronger than that of NaOl–pyrolusite. These results are consistent with flotation experiments and DFT simulation results. These results are consistent with flotation experiments and DFT simulation results.

The results of the OPS–mineral molecular dynamic simulation (Figure 12) show that in the OPS anion, the O atom is the main site of action. On the mineral surface, the Mn atom of pyrolusite and the Fe atom of hematite are active sites that interact with the O atom of the OPS anion, respectively. It is clear that the interaction between OPS and hematite is much stronger than the interaction between OPS and pyrolusite because their distances are so much farther apart. This is also supported by the results of flotation experiments and DFT simulations.

The adsorption energies were determined based on the energy of the system (E_Total), collector (E_Reagent), and mineral (E_Minal), using the following calculation procedure. The results are displayed in Figure 13.

The results indicate that the adsorption energies of the three collectors are all negative, indicating that the adsorption of the three collectors on the mineral surface can occur spontaneously. The adsorption energy values of the three types of collectors are not significantly different from those of pyrolusite; however, there are significant differences in the adsorption energy values between different collectors and hematite. The adsorption energy between OPS and hematite is the highest among the three, indicating that the interaction between OPS and hematite is the strongest. Meanwhile, there is a significant difference in adsorption energy between OPS hematite and OPS pyrolusite, indicating that OPS has strong selectivity in pyrolusite reverse flotation. The above calculation results are consistent with the flotation experiment results.

4. Conclusions

The present study has explored suitable collectors for removing hematite from pyrolusite and studied the interaction mechanism between the minerals and the collectors. The following are the results:

(1)

Flotation experiments have shown that, compared with sodium oleate and dodecyl sulfonic acid, oxidized paraffin soap has better flotation separation effects on pyrolusite and hematite.

(2)

Based on the density of state findings, the Mn atoms in pyrolusite crystals are identified as the active sites, whereas the Fe atoms in hematite crystals are identified as the active sites.

(3)

Upon investigating the energy calculation results of the frontline orbit and the adsorption energies, it was shown that the three collectors (OPS, NaOl, and SDS) have a much stronger interaction with hematite than with pyrolusite. Therefore, it is possible to separate pyrolusite and hematite through flotation.

(4)

The simulation results also show that among the three collectors, OPS has the highest adsorption strength and selectivity for hematite. This characteristic makes OPS an excellent collector for effectively removing hematite from pyrolusite in the reverse flotation process.